Overview

Dataset statistics

Number of variables12
Number of observations13393
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory1.2 MiB
Average record size in memory96.0 B

Variable types

Numeric10
Categorical2

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
height_cm is highly correlated with weight_kg and 3 other fieldsHigh correlation
weight_kg is highly correlated with height_cm and 2 other fieldsHigh correlation
body fat_% is highly correlated with height_cm and 3 other fieldsHigh correlation
diastolic is highly correlated with systolicHigh correlation
systolic is highly correlated with diastolicHigh correlation
gripForce is highly correlated with height_cm and 4 other fieldsHigh correlation
sit-ups counts is highly correlated with body fat_% and 2 other fieldsHigh correlation
broad jump_cm is highly correlated with height_cm and 4 other fieldsHigh correlation
age is highly correlated with sit-ups countsHigh correlation
height_cm is highly correlated with weight_kg and 4 other fieldsHigh correlation
weight_kg is highly correlated with height_cm and 1 other fieldsHigh correlation
body fat_% is highly correlated with height_cm and 3 other fieldsHigh correlation
diastolic is highly correlated with systolicHigh correlation
systolic is highly correlated with diastolicHigh correlation
gripForce is highly correlated with height_cm and 4 other fieldsHigh correlation
sit-ups counts is highly correlated with age and 4 other fieldsHigh correlation
broad jump_cm is highly correlated with height_cm and 3 other fieldsHigh correlation
height_cm is highly correlated with weight_kg and 1 other fieldsHigh correlation
weight_kg is highly correlated with height_cm and 1 other fieldsHigh correlation
gripForce is highly correlated with height_cm and 2 other fieldsHigh correlation
sit-ups counts is highly correlated with broad jump_cmHigh correlation
broad jump_cm is highly correlated with gripForce and 1 other fieldsHigh correlation
age is highly correlated with sit-ups countsHigh correlation
gender is highly correlated with height_cm and 5 other fieldsHigh correlation
height_cm is highly correlated with gender and 3 other fieldsHigh correlation
weight_kg is highly correlated with gender and 3 other fieldsHigh correlation
body fat_% is highly correlated with gender and 1 other fieldsHigh correlation
diastolic is highly correlated with systolicHigh correlation
systolic is highly correlated with diastolicHigh correlation
gripForce is highly correlated with gender and 4 other fieldsHigh correlation
sit-ups counts is highly correlated with age and 3 other fieldsHigh correlation
broad jump_cm is highly correlated with gender and 5 other fieldsHigh correlation
class is uniformly distributed Uniform

Reproduction

Analysis started2023-02-27 15:51:31.835092
Analysis finished2023-02-27 15:51:55.866967
Duration24.03 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct44
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.7751064
Minimum21
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-02-27T21:21:56.009859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q125
median32
Q348
95-th percentile62
Maximum64
Range43
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.62563948
Coefficient of variation (CV)0.3705125779
Kurtosis-1.017671491
Mean36.7751064
Median Absolute Deviation (MAD)9
Skewness0.599895536
Sum492529
Variance185.6580511
MonotonicityNot monotonic
2023-02-27T21:21:56.387157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
21964
 
7.2%
22789
 
5.9%
23668
 
5.0%
25644
 
4.8%
26629
 
4.7%
24617
 
4.6%
27546
 
4.1%
28527
 
3.9%
29407
 
3.0%
30374
 
2.8%
Other values (34)7228
54.0%
ValueCountFrequency (%)
21964
7.2%
22789
5.9%
23668
5.0%
24617
4.6%
25644
4.8%
26629
4.7%
27546
4.1%
28527
3.9%
29407
3.0%
30374
 
2.8%
ValueCountFrequency (%)
64215
1.6%
63230
1.7%
62265
2.0%
61254
1.9%
60368
2.7%
59192
1.4%
58180
1.3%
57181
1.4%
56197
1.5%
55185
1.4%

gender
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.8 KiB
M
8467 
F
4926 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13393
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M8467
63.2%
F4926
36.8%

Length

2023-02-27T21:21:56.538285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-27T21:21:56.722259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
m8467
63.2%
f4926
36.8%

Most occurring characters

ValueCountFrequency (%)
M8467
63.2%
F4926
36.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter13393
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M8467
63.2%
F4926
36.8%

Most occurring scripts

ValueCountFrequency (%)
Latin13393
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M8467
63.2%
F4926
36.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII13393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M8467
63.2%
F4926
36.8%

height_cm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct467
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.5598074
Minimum125
Maximum193.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-02-27T21:21:56.866221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum125
5-th percentile154.2
Q1162.4
median169.2
Q3174.8
95-th percentile181.5
Maximum193.8
Range68.8
Interquartile range (IQR)12.4

Descriptive statistics

Standard deviation8.426582551
Coefficient of variation (CV)0.04999164796
Kurtosis-0.4330534987
Mean168.5598074
Median Absolute Deviation (MAD)6.1
Skewness-0.1868823465
Sum2257521.5
Variance71.00729348
MonotonicityNot monotonic
2023-02-27T21:21:57.038695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170126
 
0.9%
173112
 
0.8%
175103
 
0.8%
171101
 
0.8%
17294
 
0.7%
16793
 
0.7%
17481
 
0.6%
172.581
 
0.6%
16879
 
0.6%
16478
 
0.6%
Other values (457)12445
92.9%
ValueCountFrequency (%)
1251
< 0.1%
139.51
< 0.1%
139.81
< 0.1%
139.91
< 0.1%
140.51
< 0.1%
1411
< 0.1%
143.41
< 0.1%
143.61
< 0.1%
143.71
< 0.1%
143.81
< 0.1%
ValueCountFrequency (%)
193.81
 
< 0.1%
1921
 
< 0.1%
191.91
 
< 0.1%
191.83
< 0.1%
191.71
 
< 0.1%
191.61
 
< 0.1%
191.42
< 0.1%
191.32
< 0.1%
190.92
< 0.1%
190.81
 
< 0.1%

weight_kg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1398
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.44731576
Minimum26.3
Maximum138.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-02-27T21:21:57.229875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum26.3
5-th percentile49.2
Q158.2
median67.4
Q375.3
95-th percentile87.3
Maximum138.1
Range111.8
Interquartile range (IQR)17.1

Descriptive statistics

Standard deviation11.94966634
Coefficient of variation (CV)0.1771703767
Kurtosis0.1716060599
Mean67.44731576
Median Absolute Deviation (MAD)8.54
Skewness0.3498045915
Sum903321.9
Variance142.7945257
MonotonicityNot monotonic
2023-02-27T21:21:57.411541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.553
 
0.4%
7151
 
0.4%
7051
 
0.4%
72.349
 
0.4%
6649
 
0.4%
73.249
 
0.4%
60.949
 
0.4%
71.848
 
0.4%
74.147
 
0.4%
72.447
 
0.4%
Other values (1388)12900
96.3%
ValueCountFrequency (%)
26.31
 
< 0.1%
31.91
 
< 0.1%
33.71
 
< 0.1%
34.41
 
< 0.1%
34.51
 
< 0.1%
35.91
 
< 0.1%
36.51
 
< 0.1%
37.31
 
< 0.1%
37.42
< 0.1%
38.14
< 0.1%
ValueCountFrequency (%)
138.11
< 0.1%
135.781
< 0.1%
125.71
< 0.1%
1231
< 0.1%
119.81
< 0.1%
119.61
< 0.1%
118.81
< 0.1%
118.61
< 0.1%
117.51
< 0.1%
117.41
< 0.1%

body fat_%
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct527
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.24016495
Minimum3
Maximum78.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-02-27T21:21:57.607403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile12.1
Q118
median22.8
Q328
95-th percentile35.74
Maximum78.4
Range75.4
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.25684408
Coefficient of variation (CV)0.3122544137
Kurtosis0.1287121896
Mean23.24016495
Median Absolute Deviation (MAD)5
Skewness0.3611322492
Sum311255.5292
Variance52.661786
MonotonicityNot monotonic
2023-02-27T21:21:57.792032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.190
 
0.7%
20.287
 
0.6%
22.887
 
0.6%
24.587
 
0.6%
20.385
 
0.6%
25.983
 
0.6%
20.582
 
0.6%
24.780
 
0.6%
18.280
 
0.6%
21.279
 
0.6%
Other values (517)12553
93.7%
ValueCountFrequency (%)
32
< 0.1%
3.53
< 0.1%
41
 
< 0.1%
4.51
 
< 0.1%
4.71
 
< 0.1%
4.93
< 0.1%
51
 
< 0.1%
5.53
< 0.1%
5.61
 
< 0.1%
5.82
< 0.1%
ValueCountFrequency (%)
78.41
< 0.1%
54.91
< 0.1%
53.51
< 0.1%
50.61
< 0.1%
50.31
< 0.1%
50.21
< 0.1%
49.81
< 0.1%
49.31
< 0.1%
49.21
< 0.1%
48.91
< 0.1%

diastolic
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct89
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.79684163
Minimum0
Maximum156.2
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-02-27T21:21:57.987946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile61
Q171
median79
Q386
95-th percentile96
Maximum156.2
Range156.2
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.7420331
Coefficient of variation (CV)0.136325681
Kurtosis0.3635245366
Mean78.79684163
Median Absolute Deviation (MAD)8
Skewness-0.159637171
Sum1055326.1
Variance115.3912751
MonotonicityNot monotonic
2023-02-27T21:21:58.176774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80670
 
5.0%
77482
 
3.6%
75475
 
3.5%
78474
 
3.5%
81464
 
3.5%
79460
 
3.4%
83448
 
3.3%
82445
 
3.3%
76428
 
3.2%
74420
 
3.1%
Other values (79)8627
64.4%
ValueCountFrequency (%)
01
 
< 0.1%
61
 
< 0.1%
81
 
< 0.1%
301
 
< 0.1%
371
 
< 0.1%
401
 
< 0.1%
412
 
< 0.1%
426
< 0.1%
432
 
< 0.1%
443
< 0.1%
ValueCountFrequency (%)
156.21
 
< 0.1%
1261
 
< 0.1%
1211
 
< 0.1%
1201
 
< 0.1%
1181
 
< 0.1%
1171
 
< 0.1%
1151
 
< 0.1%
1131
 
< 0.1%
1123
< 0.1%
1111
 
< 0.1%

systolic
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct102
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.2348167
Minimum0
Maximum201
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-02-27T21:21:58.390143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile106
Q1120
median130
Q3141
95-th percentile155
Maximum201
Range201
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.71395352
Coefficient of variation (CV)0.1129801838
Kurtosis0.3802848389
Mean130.2348167
Median Absolute Deviation (MAD)10
Skewness-0.04865360959
Sum1744234.9
Variance216.5004282
MonotonicityNot monotonic
2023-02-27T21:21:58.590468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120515
 
3.8%
130416
 
3.1%
123404
 
3.0%
134335
 
2.5%
128320
 
2.4%
118319
 
2.4%
132314
 
2.3%
125314
 
2.3%
129310
 
2.3%
122310
 
2.3%
Other values (92)9836
73.4%
ValueCountFrequency (%)
01
 
< 0.1%
141
 
< 0.1%
43.91
 
< 0.1%
771
 
< 0.1%
821
 
< 0.1%
841
 
< 0.1%
863
< 0.1%
882
< 0.1%
892
< 0.1%
902
< 0.1%
ValueCountFrequency (%)
2011
 
< 0.1%
1951
 
< 0.1%
1932
< 0.1%
1911
 
< 0.1%
1881
 
< 0.1%
1871
 
< 0.1%
1861
 
< 0.1%
1841
 
< 0.1%
1813
< 0.1%
1801
 
< 0.1%

gripForce
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct550
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.9638774
Minimum0
Maximum70.5
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-02-27T21:21:58.798760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.9
Q127.5
median37.9
Q345.2
95-th percentile53.5
Maximum70.5
Range70.5
Interquartile range (IQR)17.7

Descriptive statistics

Standard deviation10.62486403
Coefficient of variation (CV)0.2874391102
Kurtosis-0.8222001688
Mean36.9638774
Median Absolute Deviation (MAD)8.7
Skewness0.01845649338
Sum495057.21
Variance112.8877356
MonotonicityNot monotonic
2023-02-27T21:21:58.988315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.171
 
0.5%
43.967
 
0.5%
40.659
 
0.4%
40.158
 
0.4%
40.358
 
0.4%
39.358
 
0.4%
25.458
 
0.4%
27.557
 
0.4%
43.557
 
0.4%
44.557
 
0.4%
Other values (540)12793
95.5%
ValueCountFrequency (%)
03
< 0.1%
1.61
 
< 0.1%
2.11
 
< 0.1%
3.51
 
< 0.1%
4.41
 
< 0.1%
5.31
 
< 0.1%
6.71
 
< 0.1%
7.91
 
< 0.1%
8.61
 
< 0.1%
9.11
 
< 0.1%
ValueCountFrequency (%)
70.51
< 0.1%
70.41
< 0.1%
69.91
< 0.1%
691
< 0.1%
68.41
< 0.1%
67.61
< 0.1%
67.12
< 0.1%
66.81
< 0.1%
661
< 0.1%
65.81
< 0.1%

sit and bend forward_cm
Real number (ℝ)

Distinct528
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.20926827
Minimum-25
Maximum213
Zeros12
Zeros (%)0.1%
Negative642
Negative (%)4.8%
Memory size104.8 KiB
2023-02-27T21:21:59.195806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-25
5-th percentile0.2
Q110.9
median16.2
Q320.7
95-th percentile26.54
Maximum213
Range238
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation8.456677009
Coefficient of variation (CV)0.5560212928
Kurtosis35.22085641
Mean15.20926827
Median Absolute Deviation (MAD)4.9
Skewness0.7854920085
Sum203697.73
Variance71.51538604
MonotonicityNot monotonic
2023-02-27T21:21:59.373201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20119
 
0.9%
18.5102
 
0.8%
16102
 
0.8%
19100
 
0.7%
1799
 
0.7%
1598
 
0.7%
2195
 
0.7%
1492
 
0.7%
2289
 
0.7%
1887
 
0.6%
Other values (518)12410
92.7%
ValueCountFrequency (%)
-251
 
< 0.1%
-221
 
< 0.1%
-2010
0.1%
-19.92
 
< 0.1%
-19.71
 
< 0.1%
-192
 
< 0.1%
-18.91
 
< 0.1%
-18.71
 
< 0.1%
-18.41
 
< 0.1%
-181
 
< 0.1%
ValueCountFrequency (%)
2131
 
< 0.1%
1851
 
< 0.1%
421
 
< 0.1%
402
 
< 0.1%
371
 
< 0.1%
35.25
< 0.1%
351
 
< 0.1%
34.82
 
< 0.1%
34.71
 
< 0.1%
34.62
 
< 0.1%

sit-ups counts
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct81
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.77122377
Minimum0
Maximum80
Zeros125
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-02-27T21:21:59.558013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q130
median41
Q350
95-th percentile60
Maximum80
Range80
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.27669833
Coefficient of variation (CV)0.3589705564
Kurtosis-0.1563258628
Mean39.77122377
Median Absolute Deviation (MAD)10
Skewness-0.4678298773
Sum532656
Variance203.8241151
MonotonicityNot monotonic
2023-02-27T21:21:59.752095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45416
 
3.1%
40394
 
2.9%
50393
 
2.9%
46383
 
2.9%
47369
 
2.8%
48368
 
2.7%
44367
 
2.7%
43365
 
2.7%
51362
 
2.7%
42347
 
2.6%
Other values (71)9629
71.9%
ValueCountFrequency (%)
0125
0.9%
118
 
0.1%
231
 
0.2%
323
 
0.2%
428
 
0.2%
4.61
 
< 0.1%
529
 
0.2%
628
 
0.2%
736
 
0.3%
834
 
0.3%
ValueCountFrequency (%)
801
 
< 0.1%
782
 
< 0.1%
764
 
< 0.1%
754
 
< 0.1%
744
 
< 0.1%
732
 
< 0.1%
723
 
< 0.1%
7115
0.1%
7011
0.1%
6919
0.1%

broad jump_cm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct245
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.1296274
Minimum0
Maximum303
Zeros10
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size104.8 KiB
2023-02-27T21:21:59.953793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile121
Q1162
median193
Q3221
95-th percentile247
Maximum303
Range303
Interquartile range (IQR)59

Descriptive statistics

Standard deviation39.86800013
Coefficient of variation (CV)0.2096885197
Kurtosis0.002396501773
Mean190.1296274
Median Absolute Deviation (MAD)29
Skewness-0.422622556
Sum2546406.1
Variance1589.457435
MonotonicityNot monotonic
2023-02-27T21:22:00.140704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211181
 
1.4%
220176
 
1.3%
230172
 
1.3%
180161
 
1.2%
200157
 
1.2%
215144
 
1.1%
170140
 
1.0%
185140
 
1.0%
222139
 
1.0%
226139
 
1.0%
Other values (235)11844
88.4%
ValueCountFrequency (%)
010
0.1%
201
 
< 0.1%
351
 
< 0.1%
401
 
< 0.1%
431
 
< 0.1%
451
 
< 0.1%
471
 
< 0.1%
491
 
< 0.1%
502
 
< 0.1%
512
 
< 0.1%
ValueCountFrequency (%)
3031
 
< 0.1%
2991
 
< 0.1%
2952
< 0.1%
2941
 
< 0.1%
2931
 
< 0.1%
2902
< 0.1%
2881
 
< 0.1%
2862
< 0.1%
2853
< 0.1%
2841
 
< 0.1%

class
Categorical

UNIFORM

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.8 KiB
D
3349 
C
3349 
A
3348 
B
3347 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13393
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowA
3rd rowC
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
D3349
25.0%
C3349
25.0%
A3348
25.0%
B3347
25.0%

Length

2023-02-27T21:22:00.325908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-27T21:22:00.488380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
c3349
25.0%
d3349
25.0%
a3348
25.0%
b3347
25.0%

Most occurring characters

ValueCountFrequency (%)
C3349
25.0%
D3349
25.0%
A3348
25.0%
B3347
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter13393
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C3349
25.0%
D3349
25.0%
A3348
25.0%
B3347
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13393
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C3349
25.0%
D3349
25.0%
A3348
25.0%
B3347
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C3349
25.0%
D3349
25.0%
A3348
25.0%
B3347
25.0%

Interactions

2023-02-27T21:21:53.585072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:38.728744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:40.322744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:41.915828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:43.646731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:45.313590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:46.928444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:48.747913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:50.362703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:51.920511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:53.732828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:38.892746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:40.471072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:42.062340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:43.794666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:45.462053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:47.080487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:48.892034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:50.501619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:52.068128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:53.890108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:39.053019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:40.632061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-02-27T21:21:43.963021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-02-27T21:21:49.058800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:50.655615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-02-27T21:21:54.042754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:39.187301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:40.773005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:42.359932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:44.125119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:45.769213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:47.391732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:49.207819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:50.799944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:52.401236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:54.209633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-02-27T21:21:40.935524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:42.550202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:44.293750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:45.939721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:47.572778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-02-27T21:21:50.976041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:52.577027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:54.376710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-02-27T21:21:41.101101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-02-27T21:21:44.481633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-02-27T21:21:47.755071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:49.548070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:51.135029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-02-27T21:21:46.280947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:47.928881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:49.716055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:51.302058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-02-27T21:21:46.440052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:48.092367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:49.874017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:51.472062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:53.099532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:54.860650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:40.013149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:41.584221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:43.316849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:44.978007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:46.594573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:48.246921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:50.031585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:51.605733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:53.256587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:55.034510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:40.167946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:41.756760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:43.486577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:45.151862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:46.763018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:48.418152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:50.201976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:51.769046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-27T21:21:53.426905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-27T21:22:00.626857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-02-27T21:22:00.889729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-02-27T21:22:01.127262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-02-27T21:22:01.349779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-02-27T21:22:01.507625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-02-27T21:21:55.293737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-27T21:21:55.702659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

agegenderheight_cmweight_kgbody fat_%diastolicsystolicgripForcesit and bend forward_cmsit-ups countsbroad jump_cmclass
027.0M172.375.2421.380.0130.054.918.460.0217.0C
125.0M165.055.8015.777.0126.036.416.353.0229.0A
231.0M179.678.0020.192.0152.044.812.049.0181.0C
332.0M174.571.1018.476.0147.041.415.253.0219.0B
428.0M173.867.7017.170.0127.043.527.145.0217.0B
536.0F165.455.4022.064.0119.023.821.027.0153.0B
642.0F164.563.7032.272.0135.022.70.818.0146.0D
733.0M174.977.2036.984.0137.045.912.342.0234.0B
854.0M166.867.5027.685.0165.040.418.634.0148.0C
928.0M185.084.6014.481.0156.057.912.155.0213.0B

Last rows

agegenderheight_cmweight_kgbody fat_%diastolicsystolicgripForcesit and bend forward_cmsit-ups countsbroad jump_cmclass
1338325.0M170.768.8613.360.0106.039.214.151.0235.0B
1338464.0F152.455.9033.187.0158.023.520.014.0154.0B
1338537.0M177.583.1029.777.0113.041.77.241.0167.0D
1338662.0F156.240.0020.261.0115.018.55.71.081.0D
1338739.0M174.470.8024.378.0132.041.612.044.0168.0B
1338825.0M172.171.8016.274.0141.035.817.447.0198.0C
1338921.0M179.763.9012.174.0128.033.01.148.0167.0D
1339039.0M177.280.5020.178.0132.063.516.445.0229.0A
1339164.0F146.157.7040.468.0121.019.39.20.075.0D
1339234.0M164.066.1019.582.0150.035.97.151.0180.0C

Duplicate rows

Most frequently occurring

agegenderheight_cmweight_kgbody fat_%diastolicsystolicgripForcesit and bend forward_cmsit-ups countsbroad jump_cmclass# duplicates
027.0F157.049.130.770.086.027.719.751.0167.0A2